Art functions as a reflection of our thoughts, our feeling, and our emotions. It can embody a specific time in our lives and the relationships we experience during that time. The human mind automatically makes associations between art (audio or visual) and personal experiences or memories. For the purposes of this description, any association between a piece of art and memory may be referred to as a “cognitive link”. Such memories include, but are not limited to, people, places, events, periods of time, thought and feelings. These cognitive links mutually impact both human memory on one side of the link and the enjoyment of art on the other side of the link. As such:
Art can trigger personal and sentimental thoughts and memories that enhance the enjoyment of said art by intensifying its emotional impact.
Personal and sentimental thoughts and memories can trigger the remembrance of specific pieces of art that in turn can further enhance and stimulate memories associated with said art.
For example, hearing a specific piece of music might remind you of a time when you heard the song with your spouse thus eliciting a feeling of love and warmth. These feelings in turn will give sentimental importance to the music thus intensifying the enjoyment of the listening experience. As another example, while remembering an important event in your life you may remember hearing a specific song being played. By listening to the song, you can further stimulate memories of where you were and what you were doing while that song was playing in that specific event. Listening to other songs played during that time may help to stimulate other memories not appreciated otherwise. These cognitive links can be so strong as to last a lifetime. However, while the human mind innately makes these cognitive links between life and art, it is largely inefficient at retrieving them in a clear and well-organized fashion. These links remain largely in our subconscious, only surfacing when stimulated by a piece of art or a specific memory. These links are strong though and can remain even after a memory has faded completely from our conscious minds. It is not uncommon to hear a song and be reminded of a time long in the past.
Current technical systems for the organization artistic media utilize a hierarchical system based on attributes of the art itself such as genre, author, album, and song title for a piece of music. While such a system is convenient for easily looking for a specific of art, it fails to take into account emotional or sentimental effects on a listener, which is often the reason a user chooses specific pieces of music. As a result, while current systems facilitate organization, retrieval, and playing of individual pieces of art, they fail to directly facilitate or enhance the human mind's innate cognitive approach to art that gives art its sentimental impact.
Recent advancements in music analysis have focused on using elements of a song such as rhythm, tempo, and cadences to determine the general “mood” of a specific piece. This has been utilized as a means of recommending music based on matching the “mood” of a song with the “mood” of a listener, or alternatively, using the “mood” of a song contrary to that of the listener to help sway or change the “mood” of the listener. While this may function well when a song is heard for the first time, as a listener hears a song more frequently they automatically develop cognitive associates between the music and details of their personal life. Ultimately, the emotional impact of music goes deeper than technical elements; more important are those memories an individual associates with a specific piece. As such, an up-beat, “happy” song may remind a listener of a loved one who recently passed away. The emotions evoked from hearing that song may be intense sadness and loss (even though the song is considered a “happy” song). Years later, it may elicit a sense of sadness with component of nostalgia. Years later still, it may elicit a sense of happiness, reminding the listener of the good times they once had with their loved one. Hence, a single cognitive link between a song and a person may result in a wide variety of emotional responses that may change over time. Cognitive links will always supersede musical elements in eliciting emotional responses. Music therefore has its greatest impact when cognitive links are in emotional synchrony with the music's “mood”.
The current method for bridging the gap between music- or mood-based, hierarchical organization of music and the personal, cognitive connections of music is the through the use of “playlists”. Playlists in these regard are simple lists of songs that are jointly given a specific name. Playlist will often focus on a set of cognitive links to music so as to allow for easy retrieval of that music and its associated cognitive links. However, the “theme” or “cognitive link(s)” are fixed when the playlist is created. Playlists are ultimately limited to the finite list of songs that are added to them based on those songs that come to mind during the creation of the playlist or added post hoc. Any minor modification, deviation, or subcategorization of the theme requires the generation of an entirely new playlist. In addition, playlists become increasingly difficult to manage the longer they get and the more playlists an individual has. Ultimately, maintaining an extensive playlist library is difficult and tedious. Current efforts have focused on making it easier to create and modify playlists, however, this fails to solve the above issues.
Music is inherently a social phenomenon and tends to play an important role in social events. Music can help connect us to the people, the place, the feelings, etc., of the event. We experience events in our lives using all sensory modalities; similarly, events are best remembered when multiple sensory modalities can be re-experienced. Systems exist for recording photos, videos, comments, and thoughts about an event. Playlists can be generated to provide the music of an event. However, there are currently no systems which gather all the available data, between multiple attendees, into a single place and displays it in such a way as to allow for the comprehensive, multi-sensory experience of an event need for optimal re-experiencing of said event. Similarly, our memories of music, people, places, events, eras, etc. are also multisensory. There are currently no systems available for bringing together all our important memories and media about these into a single place and displaying them in such a way as to allow for the comprehensive, multi-sensory experience needed for optimal re-experiencing.
Another important humanistic element of art is that its enjoyment is enhanced when people experience art together. While radio is able to accomplish this, individual listeners have no control over what is played or when. While some new companies have developed ways of sharing playlists or artistic likes/dislikes, there is currently no means to experience personally generated art together across distances on different networks.
Various types of “radio” stations exist which play a variety of music selections. Music selections are primarily chosen completely by a third party, whether it is a person or selected at random based on a mathematical algorithms. An ideal algorithm would take into account the user's personal connection to music and specific attributes that the user finds appealing to allow for a very personalized music recommendation system. Additionally, having the ability to fine-tune the music selected by the algorithm to a listener's specific music needs at that moment in time is also ideal. However, current algorithms tend to be largely static. Algorithms may be focus on a single song, artists or a group of songs. Alternatively, algorithms may focus on attributes inherent to the music itself such as tempo, beat, cadence, etc. User input is almost exclusively limited to general likes and dislikes with the user providing “thumbs up” or “thumbs down”. This ultimately results in a system that tries to predict what you might like based on attributes of songs that you tell the system you do like. However, this misses a critical piece of information—why exactly you like the songs that you like. A system that understands why you like a specific song will be more likely to successfully predict what you might like to hear next.